Using Deep Reinforcement Learning to Enhance Channel Sampling Patterns in Integrated Sensing and Communication

TL;DR

This paper introduces a deep reinforcement learning framework that optimizes channel sampling patterns in ISAC systems to improve micro-Doppler spectrogram reconstruction, adapting to variable traffic with higher accuracy and lower complexity.

Contribution

It is the first to learn sampling patterns that directly optimize micro-Doppler quality in ISAC, leveraging temporal evolution for adaptive sensing.

Findings

Up to 40% higher micro-Doppler reconstruction accuracy.

Significantly lower computational complexity than existing methods.

Effective adaptation to variable communication traffic.

Abstract

In Integrated Sensing And Communication (ISAC) systems, estimating the micro-Doppler (mD) spectrogram of a target requires combining channel estimates retrieved from communication with ad-hoc sensing packets, which cope with the sparsity of the communication traffic. Hence, the mD quality depends on the transmission strategy of the sensing packets, which is still a challenging problem with no known solutions. In this letter, we design a deep Reinforcement Learning (RL) framework that fragments such a problem into a sequence of simpler decisions and takes advantage of the mD temporal evolution for maximizing the reconstruction performance. Our method is the first that learns sampling patterns to directly optimize the mD quality, enabling the adaptation of ISAC systems to variable communication traffic. We validate the proposed approach on a dataset of real channel measurements, reaching up to 40% higher mD reconstruction accuracy and several times lower computational complexity than state-of-the-art methods.